The increasing demand for accessible and intelligent healthcare support systems has accelerated the adoption of Artificial Intelligence (AI) technologies in clinical assistance and disease prediction applications. Conventional symptom-checking systems often suffer from limited contextual understanding, static response generation, and inadequate conversational adaptability, which restrict their effectiveness in real-time healthcare environments. This research presents a Deep Neural Network (DNN)–driven conversational clinical decision support system that integrates Natural Language Processing (NLP) techniques with intelligent healthcare dialogue mechanisms for automated symptom interpretation and preliminary disease prediction. The proposed framework employs advanced NLP preprocessing methods including tokenization, stop-word elimination, stemming, lemmatization, Part-of-Speech (POS) tagging, and Named Entity Recognition (NER) to transform unstructured user symptom descriptions into machine-readable representations. A transformer-assisted deep learning architecture is incorporated to analyze symptom patterns and generate context-aware healthcare recommendations through conversational interaction. The experimental evaluation was conducted on a healthcare dataset containing more than 10,000 symptom records associated with multiple disease categories and clinical conditions. The developed model achieved an overall prediction accuracy of 96.4%, with improved precision, recall, and conversational response quality compared to traditional machine learning and rule-based healthcare chatbot systems. The system also demonstrated reduced response latency and enhanced contextual understanding during multi-turn clinical conversations. The major contribution of this work lies in the integration of adaptive conversational intelligence with deep neural clinical reasoning to provide scalable, real-time, and user-centric healthcare assistance for preliminary medical guidance and decision support.

Keywords: Natural Language Processing (NLP), Deep Neural Networks (DNN), Clinical Decision Support System, Medical Chatbot, Symptom Analysis, Conversational AI, Disease Prediction, Healthcare Informatics